75 citations · 232 across the 14 of their papers we have counts for
15 papers
MS-ECG-FM: Towards a More Universal Electrocardiogram Foundation Model for Health Monitoring using Multi-source Contrastive Learning
Robert A. Lewis, I-Min Chiu, Kyle Verrier +7
Electrocardiography (ECG) records the electrical activity of the heart, aiding diagnosis by detecting abnormalities in cardiac function. ECG foundation models have demonstrated pro…
Wrist Photoplethysmography Predicts Dietary Information
Kyle Verrier, Achille Nazaret, Joseph Futoma +2
Whether wearable photoplethysmography (PPG) contains dietary information remains unknown. We trained a language model on 1.1M meals to predict meal descriptions from PPG, aligning…
Beyond Sensor Data: Foundation Models of Behavioral Data from Wearables Improve Health Predictions
Eray Erturk, Fahad Kamran, Salar Abbaspourazad +6
Wearable devices record physiological and behavioral signals that can improve health predictions. While foundation models are increasingly used for such predictions, they have been…
Wearable Accelerometer Foundation Models for Health via Knowledge Distillation
Salar Abbaspourazad, Anshuman Mishra, Joseph Futoma +2
Modern wearable devices can conveniently record various biosignals in the many different environments of daily living, enabling a rich view of individual health. However, not all b…
Label Shift Estimators for Non-Ignorable Missing Data
Andrew C. Miller, Joseph Futoma
We consider the problem of estimating the mean of a random variable Y subject to non-ignorable missingness, i.e., where the missingness mechanism depends on Y . We connect the auxi…
Model-based metrics: Sample-efficient estimates of predictive model subpopulation performance
Andrew C. Miller, Leon A. Gatys, Joseph Futoma +1
Machine learning models now commonly developed to screen, diagnose, or predict health conditions are evaluated with a variety of performance metrics. An important first ste…
Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs
Jianzhun Du, Joseph Futoma, Finale Doshi-Velez
We present two elegant solutions for modeling continuous-time dynamics, in a novel model-based reinforcement learning (RL) framework for semi-Markov decision processes (SMDPs), usi…
Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions
Omer Gottesman, Joseph Futoma, Yao Liu +4
Off-policy evaluation in reinforcement learning offers the chance of using observational data to improve future outcomes in domains such as healthcare and education, but safe deplo…
Identifying Distinct, Effective Treatments for Acute Hypotension with SODA-RL: Safely Optimized Diverse Accurate Reinforcement Learning
Joseph Futoma, Muhammad A. Masood, Finale Doshi-Velez
Hypotension in critical care settings is a life-threatening emergency that must be recognized and treated early. While fluid bolus therapy and vasopressors are common treatments, i…
POPCORN: Partially Observed Prediction COnstrained ReiNforcement Learning
Joseph Futoma, Michael C. Hughes, Finale Doshi-Velez
Many medical decision-making tasks can be framed as partially observed Markov decision processes (POMDPs). However, prevailing two-stage approaches that first learn a POMDP and the…
"The Human Body is a Black Box": Supporting Clinical Decision-Making with Deep Learning
Mark Sendak, Madeleine Elish, Michael Gao +6
Machine learning technologies are increasingly developed for use in healthcare. While research communities have focused on creating state-of-the-art models, there has been less foc…
An Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis Detection
Joseph Futoma, Sanjay Hariharan, Mark Sendak +5
Sepsis is a poorly understood and potentially life-threatening complication that can occur as a result of infection. Early detection and treatment improves patient outcomes, and as…
Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier
Joseph Futoma, Sanjay Hariharan, Katherine Heller
We present a scalable end-to-end classifier that uses streaming physiological and medication data to accurately predict the onset of sepsis, a life-threatening complication from in…
Scalable Modeling of Multivariate Longitudinal Data for Prediction of Chronic Kidney Disease Progression
Joseph Futoma, Mark Sendak, C. Blake Cameron +1
Prediction of the future trajectory of a disease is an important challenge for personalized medicine and population health management. However, many complex chronic diseases exhibi…
A unifying representation for a class of dependent random measures
Nicholas J. Foti, Joseph D. Futoma, Daniel N. Rockmore +1
We present a general construction for dependent random measures based on thinning Poisson processes on an augmented space. The framework is not restricted to dependent versions of…